Search

In the literature, several approaches have been proposed to integrate and optimize product supply and construction processes associated with demand management. However, in Industry 4.0, there needs to be more studies related to applying techniques that directly affect the programming and reprogramming process that integrates the industries at the operational level. This document proposes a flow-shop scheduling procedure to address the problem of planning the repair of medical equipment in public hospitals whose main objective is to eliminate downtime and minimize total production time. The research stems from the practical problem of responding to clinical users who make use of critical equipment, such as mechanical respirators, due to COVID-19, and the limited quantity of this equipment, which makes it necessary to have repair planning processes that seek to keep the equipment in operation for the most extended amount of time. The novelty of this study is that it was applied to a critical and real problem in the industry with a high economic and social impact, which has not been explored previously. The results show improvements in the overall planning and execution of electro-medical equipment repair. Several improvements to the applied methods were identified as future work, such as the need to consider work interruptions and psychosocial effects on workers due to the stricter planning of execution times.

This work proposes the use of a constructive heuristic called Shifting Bottleneck Procedure for solving the Job-Shop Scheduling problem, with the aim to minimize the makespan, that is, the final delay of production planning. Because the constructive heuristic delivers an initial solution, then a Tabu Search method was used, with the aim to obtain a planification with the lowest possible makespan. The proposal was tested using benchmark data, and the results are promising, because the obtention of initial solutions using a constructive heuristic allows to decrease the processing time, and usually, the final results obtained from Tabu Search method are better.

Hyper-heuristics are optimization techniques for solving hard combinatorial problems. Their main feature is that their design involves an important decoupling of the search components from the problem domain ones. This allows them to extend their applicability to different problem domains without major redesign, unlike traditional methods such as metaheuristics. In this work, a hyper-heuristic is evaluated for a transportation problem. The implemented hyper-heuristic uses a greedy operator, and it implements an adapter layer that would allow it to be used in other similar problems. Experimental results shows balanced solution quality and CPU time performance, regarding other metaheuristics in literature.

Hyper-heuristics are methods for problem solving that decouple the search mechanisms from the domain features, providing a reusable approach across different problems. Even when they make a difference regarding metaheuristics under this perspective, proposals in literature commonly expose parameters for controlling their behavior such as metaheuristics does. Several internal mechanisms for automatically adapt those parameters can be implemented, but they require extra design effort and their validation no necessarily is generalizable to multiple domains. Such effort is prohibitive for their practical application on decision-support systems. Rather than implementing internal adapting mechanisms, the exploration of automatic parameter configuration through external tools is performed in this work. A new hyper-heuristic implementation based on a elite set of solutions was implemented and automatically configured with SMAC (Sequential Model-Based Algorithm Configuration), a state-of-art tool for automatic parameter configuration. Experiments with and without automated configuration are performed over the Multidimensional Knapsack Problem (MKP). Comparative results demonstrate the effectiveness of the tool for improving the algorithm performance. Additionally, results provided insights that configurations applied over subsets of instances could provide better improvements in the algorithm performance.

Yard Allocation Problem (YAP) is a frequently problem observed in port logistic operations, and consists in a temporary assignment of cargo containers, both export and import, to blocks storage located in the operation zone of seaports. The principal objective of YAP resolution is to improve the operations efficiency both terminal port as shipping companies that use the seaport, which allow to decrease operational cost and increasing the global efficiency. In this sense, this work proposes to solve the YAP using a constructive algorithm based in restrictions for each container. Official data of Arica Seaport — Chile were used for defining an experimental scenario, which allowed obtaining promising results. The constructive algorithm represents a novel approach because allows getting solutions in a simple and fast way, while satisfying the existing set of constrains associated with the general operation model.

The dial-a-ride problem with time windows (DARPTW) is a combinatorial optimization problem related to transportation, in which a set of customers must be picked up from an origin location and they have to be delivered to a destination location. A transportation schedule must be constructed for a set of available vehicles, and several constraints have to be considered, particularly time windows, which define an upper and lower time bound for each customer request in which a vehicle must arrive to perform the service. Because of the complexity of DARPTW, a number of algorithms have been proposed for solving the problem, mainly based on metaheuristics such as Genetic Algorithms and Simulated Annealing. In this work, a different approach for solving DARPTW is proposed, designed, and evaluated: hyperheuristics, which are alternative heuristic methods that operate at a higher abstraction level than metaheuristics, because rather than searching in the problem space directly, they search in a space of low-level heuristics to find the best strategy through which good solutions can be found. Although the proposed hyperheuristic uses simple and easy-to-implement operators, the experimental results demonstrate efficient and competitive performance on DARPTW when compared to other metaheuristics from the literature.

The problem of demand-responsive passenger transportation is a subject covered from different perspectives (such as planning, user interfaces, optimization problem, etc). This work is focused on the simulation of events occurring in real time. That is, as requests are generated by vehicle users and the planning moves forward in time events may occur that affect them so that there should be a re-planning procedure. Among the diverse considered events can be considered: user delays and cancellations of requests, failure of vehicles in operation, among others. Under these premises is given a software architecture based on agents that is capable of supporting such events and hence perform the replanning. This is a description of the architecture, design and implementation by using intelligent agents under the PASSI methodology.

This work tackles the problem of providing a mechanism and infrastructure for allowing a given Multiagent System (MAS) to become open, allowing the incorporation of newly incoming agents to participate within the existing society. For this, a conceptual analysis of the so-called conciliation problem is presented, covering the diverse levels and issues involved in such a process. Our Dynamic Incorporation Architecture is presented, which implements an infrastructure for allowing the participation of external agents into a specific multiagent system by incorporating the appropriate behaviours upon arrival. Our multiagent architecture for dynamic incorporation covers three levels: semantics, communication and interaction and has been applyed in a book-trading e-market scenario.

The present work focuses in the implementation of an open system for book-trading based in the Agent paradigm. For enabling openness an agent infrastructure is provided allowing at execution time a newly incoming buyer agent to conciliate its way of interacting with the system rather than being done at design time. The proposal considers the disambiguation of the possible divergences that could arise at three levels: semantics, communication and interaction.